Detecting and Mitigating DDoS Attacks with AI: A Survey
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Abstract (arXiv)
Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 120 references that are in the map. First posted on arXiv 22 Mar 2025.
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20 of 120 references.
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